We train and test the GIP model following the partitions of train, validation and test data in [1]}, [2]}. We use random sampling, preserving the ratio of images in the train and the validation sets.
We perform 3-fold cross-validation and train for a total of 50 epochs using a batch size of 64. The categorical cross-entropy loss is optimised with the Adam [3]} optimiser.
We use PyTorch [4]} 1.8.1 and a single 32GB Tesla V100 GPU with an initial learning rate of 0.0001.
